DeepSeek V4.1 Flash vs OpenVLA 7B
At a Glance
| Compare |  DeepSeek V4.1 FlashDeepSeek |  OpenVLA 7BOpenVLA Research Team |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| CostLower is better · Published-token output estimate | #5 of 44$0.022 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| Pricing and Limits | ||
| Context windowMaximum documented tokens | 1,049K | Not reported |
| Model facts checked | Sep 10, 2026View model evidence → | Aug 29, 2026View model evidence → |
Available Benchmarks
Side-by-Side Facts
| Field | DeepSeek-V4.1-Flash | OpenVLA 7B |
|---|---|---|
| Developer | DeepSeek | OpenVLA Research Team |
| Family | Deepseek V4 1 | OpenVLA |
| Model | DeepSeek-V4.1-Flash | OpenVLA 7B |
| Version | DeepSeek-V4.1-Flash | 7B |
| Lifecycle | active | active |
| Released | 2026-09-10 | 2024-06-13 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image, Robot state |
| Output modalities | Text | Robot action |
| Context window | 1,049K | Unknown |
| Total parameters | 763.2B | 7B |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | Yes |
| API available | Yes | No |
| Self-hostable | Yes | Yes |
| Provider access | DeepSeek (Standard), Deepinfra (Standard), Together Ai (Standard) | Unknown |
| Capabilities | agents, chat, fim, generation, reasoning, responses, structured_outputs, tools, vision | cross-embodiment, fine-tuning, generalist-manipulation |
| Architecture design | Causal Encoder-Decoder (20 encoder + 20 decoder layers) | Unknown |
| Backbone parameters | 552000000000 parameters | Unknown |
| Active parameters during decode | 16000000000 parameters | Unknown |
| Active parameters during prefill | 8000000000 parameters | Unknown |
| Pre-training corpus | 45000000000000 tokens | Unknown |
| Reasoning effort range | 1–100 | Unknown |
| Routed experts per MoE layer | 384 experts | Unknown |
| Routed experts per token | 6 experts | Unknown |
| Transformer layers | 40 layers | Unknown |
| Robotics model type | Unknown | Vision-language-action model |
| Action representation | Unknown | Tokenized actions decoded to continuous robot controls |
| Control architecture | Unknown | Fused SigLIP and DINOv2 visual encoder with Llama 2 7B backbone |
| Inference location | Unknown | Flexible |
| Native control rate (Hz) | Unknown | Unknown |
| Supported embodiments | Unknown | WidowX, Google Robot, Franka Panda |
| Training data | Unknown | 970,000 robot manipulation trajectories from Open X-Embodiment described by the authors. |
DeepSeek V4.1 Flash Capabilities
OpenVLA 7B Capabilities
Related Comparisons
| A | Pair | B | Context |
|---|---|---|---|
DeepSeek-V4-FlashDeepSeek | vs | DeepSeek-V4.1-FlashDeepSeek | family variantstext |
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Claude Fable 5.1Anthropic | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Gemini 3.1 ProGoogle DeepMind | cross-developer peerstext |
DeepSeek-V4.1-FlashDeepSeek | vs | Grok 4.6xAI | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Qwen3.8-MaxQwen | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Kimi-K3Moonshot AI | cross-developer peersimage, text |
 MiniMax-M3MiniMax | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  GLM-5.3Z.ai | cross-developer peerstext |
DeepSeek-V4.1-FlashDeepSeek | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | DeepSeek-V4.1-FlashDeepSeek | cross-developer peersimage, text |
DeepSeek-V4.1-FlashDeepSeek | vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
DeepSeek V4.1 Flash vs OpenVLA 7B FAQs
Is DeepSeek V4.1 Flash or OpenVLA 7B better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both DeepSeek V4.1 Flash and OpenVLA 7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, DeepSeek V4.1 Flash or OpenVLA 7B?+
Only DeepSeek V4.1 Flash has a directly sourced input price: $0.15 per million tokens. Only DeepSeek V4.1 Flash has a directly sourced output price: $0.60 per million tokens.
Which has a larger context window, DeepSeek V4.1 Flash or OpenVLA 7B?+
Neither model has a larger sourced context window in this comparison. DeepSeek V4.1 Flash is 1,049K and OpenVLA 7B is —.
Which performs better in benchmarks, DeepSeek V4.1 Flash or OpenVLA 7B?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can DeepSeek V4.1 Flash or OpenVLA 7B be self-hosted?+
Both models have the same recorded self-hosting status: supported. DeepSeek V4.1 Flash is open weight; OpenVLA 7B is open weight.
Can DeepSeek V4.1 Flash and OpenVLA 7B understand images?+
DeepSeek V4.1 Flash is documented with image input; OpenVLA 7B is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, DeepSeek V4.1 Flash or OpenVLA 7B?+
Neither has a larger sourced maximum output. DeepSeek V4.1 Flash is 393K and OpenVLA 7B is —.
Do DeepSeek V4.1 Flash and OpenVLA 7B support reasoning and tool use?+
DeepSeek V4.1 Flash: reasoning, tool calling, and image input. OpenVLA 7B: image input. Feature support does not establish relative quality.
Which is available from more inference providers, DeepSeek V4.1 Flash or OpenVLA 7B?+
DeepSeek V4.1 Flash has 3 sourced provider routes; OpenVLA 7B has 0, so DeepSeek V4.1 Flash has broader tracked availability.
Which offers better value, DeepSeek V4.1 Flash or OpenVLA 7B?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.